David Gerard

The paper develops and validates statistical tests for segregation distortion in higher ploidy F1 populations, showing improved type I error control and power.

The paper develops improved statistical tests for segregation distortion in tetraploid F1 populations by incorporating double reduction, preferential pairing, and genotype uncertainty, showing better performance than existing methods.

The paper introduces Bayesian methods to test for random mating in autopolyploids, improving upon frequentist approaches by handling small samples and genotype uncertainty.

The paper develops methods to test for genetic equilibrium and estimate double reduction in autopolyploid populations, implementing them in the hwep R package.

This paper introduces scalable moment-based methods to correct linkage disequilibrium estimates under genotype uncertainty, achieving accuracy comparable to maximum likelihood methods while being faster than naive approaches.

The paper develops methods to estimate linkage disequilibrium in polyploids, accounting for genotype uncertainty and providing standard errors for LD estimates.

This paper introduces a method to simulate RNA-seq data by adding signal to real datasets, enabling more realistic method evaluation than theoretical models.

This paper introduces two prior genotype distribution classes for polyploid genotyping, showing they improve performance compared to flexible or restrictive alternatives.

This paper develops a method for genotyping polyploid organisms using NGS data, incorporating error modeling and Mendelian segregation, and applies it to hexaploid sweet potatoes.